{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5d8459ca-6c88-4a16-8c94-780a14319667",
   "metadata": {},
   "source": [
    "# BiLSTM 多变量时间序列预测(PyTorch版)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e650f06c-9d7a-4702-988b-e73ea14a6efa",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= '#D8BFD8'>在数据科学领域中，时间序列预测一直是一个重要且挑战性的任务。随着深度学习技术的兴起，特别是循环神经网络（RNN）及其变种如长短期记忆网络（LSTM）和门控循环单元（GRU）的引入，时间序列预测的性能得到了显著提升。在这些模型中，双向长短期记忆网络（BiLSTM）因其能够同时捕获序列中的前向和后向信息，而在许多应用中表现出色。</font>\n",
    "\n",
    "<font face= 'Comic Sans MS' size= 3 color= '#D8BFD8'>在本文中，我们将探讨如何使用PyTorch框架实现基于双向长短期记忆网络（BiLSTM）的多变量时间序列预测模型。多变量时间序列预测涉及多个输入变量和一个或多个输出变量，这些变量随时间变化并可能相互影响。通过利用BiLSTM的双向特性，我们的模型将能够同时考虑历史数据和未来趋势，从而更准确地预测时间序列的未来值。</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e70d78c1-d153-4b0f-83cf-5110257f7442",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torchinfo import summary\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4aa795be-2fbe-4213-9464-56e9668dd6fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1f5c9218d10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "torch.manual_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64a97d83-6af5-46d2-9856-65382cf76dea",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#b8a1cf'>Data preprocessing</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f16ce31e-f5fa-4538-baef-c2742f18303e",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= 'orange'>**pandas.to_datetime**</font> <font face= 'Comic Sans MS' size= 3 color= '#D8BFD8'>function converts a scalar, array-like, Series or DataFrame/dict-like to a pandas datetime object.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c20ca98f-3b2f-45ba-8931-8b6c4fc8886e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.float64'> <class 'str'>\n",
      "<class 'numpy.float64'> <class 'pandas._libs.tslibs.timestamps.Timestamp'>\n",
      "(755, 6)\n"
     ]
    }
   ],
   "source": [
    "AAPL = pd.read_csv('AAPL.csv')\n",
    "print(type(AAPL['Close'].iloc[0]),type(AAPL['Date'].iloc[0]))\n",
    "# Let's convert the data type of timestamp column to datatime format\n",
    "AAPL['Date'] = pd.to_datetime(AAPL['Date'])\n",
    "print(type(AAPL['Close'].iloc[0]),type(AAPL['Date'].iloc[0]))\n",
    "\n",
    "# Selecting subset\n",
    "cond_1 = AAPL['Date'] >= '2021-04-23 00:00:00'\n",
    "cond_2 = AAPL['Date'] <= '2024-04-23 00:00:00'\n",
    "AAPL = AAPL[cond_1 & cond_2].set_index('Date')\n",
    "print(AAPL.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a1ee0b3-5cbe-465e-ba42-19b7556d0a6a",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#b8a1cf'>Data visualisation</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa1e9596-8342-461b-8399-d85925d74f3e",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= '#D8BFD8'>The closing price is the last price at which the stock is traded during the regular trading day. A stock’s closing price is the standard benchmark used by investors to track its performance over time.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4799310-296d-47cd-acea-4c488a65bdb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.style.available\n",
    "plt.style.use('_mpl-gallery')\n",
    "plt.figure(figsize=(18,6))\n",
    "plt.title('Close Price History')\n",
    "plt.plot(AAPL['Close'],label='AAPL')\n",
    "plt.ylabel('Close Price USD ($)', fontsize=18)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ba7f06-84a7-4a37-9ddf-87f71f6b38bb",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#b8a1cf'>Feature Engineering</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4351f13a-876e-4432-8a56-1caba0e6b6c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置时间回溯窗口大小\n",
    "window_size = 60"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e851f5-a97e-4f55-ad44-853af8a6ca70",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= '#D8BFD8'>The function takes two arguments: the dataset, which is a NumPy array you want to convert into a dataset, and the lookback, which is the number of previous time steps to use as input variables to predict the next time period—in this case, defaulted to 1.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29657022-03d2-421c-86a1-569de3ccaf71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构造序列数据函数，若target为 Close，即索引为 3\n",
    "def create_dataset(dataset, lookback= 1):\n",
    "    X, y = [], []\n",
    "    for i in range(len(dataset)-lookback): \n",
    "        feature = dataset[i:(i+lookback), :]\n",
    "        target = dataset[i + lookback, 3]\n",
    "        X.append(feature)\n",
    "        y.append(target)\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65d0b43b-cf91-4cf6-8c35-569cb41a5c8b",
   "metadata": {},
   "source": [
    " ## <font face= 'Comic Sans MS' size= 5 color= '#b8a1cf'>Feature Scaling (normalisation)</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "196a6264-411f-4a68-9744-a5feea460746",
   "metadata": {},
   "source": [
    "`MinMaxScaler()` 函数主要用于将特征数据按比例缩放到指定的范围。默认情况下，它将数据缩放到[0, 1]区间内，但也可以通过参数设置将数据缩放到其他范围。在机器学习中，`MinMaxScaler()`函数常用于不同尺度特征数据的标准化，以提高模型的泛化能力。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf65e0ea-8f37-4db4-8ad9-bf6df686500f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 选取 AAPL[['Open', 'High', 'Low', 'Close']]作为特征, 归一化数据\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_data = scaler.fit_transform(AAPL[['Open', 'High', 'Low', 'Close']].values)\n",
    "\n",
    "# 获取反归一化参数(即原始数据的最小值和最大值)\n",
    "original_min = scaler.data_min_\n",
    "original_max = scaler.data_max_\n",
    "\n",
    "# scale_params是一个包含所有特征反归一化参数的列表或数组，\n",
    "# 其中第四个元素是Close价格的反归一化参数\n",
    "scale_params = original_max - original_min"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a8cb95f-3a39-445a-8e76-2d4471a6dd83",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#b8a1cf'>Data set segmentation (TimeSeriesSplit)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c7aaf92-38b9-43b7-ac17-cbf8b8a597ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(695, 60, 4) (695,)\n"
     ]
    }
   ],
   "source": [
    "# 创建数据集,数据形状为 [samples, time steps, features]\n",
    "X, y = create_dataset(scaled_data, lookback = window_size)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b297b14-1d04-4a59-8cf2-27ba99a58d70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(605, 60, 4) (90, 60, 4) (605,) (90,)\n"
     ]
    }
   ],
   "source": [
    "# 使用TimeSeriesSplit划分数据集，根据需要调整n_splits\n",
    "tscv = TimeSeriesSplit(n_splits=3, test_size=90)\n",
    "# 遍历所有划分进行交叉验证\n",
    "for i, (train_index, test_index) in enumerate(tscv.split(X)):\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    # print(f\"Fold {i}:\")\n",
    "    # print(f\"  Train: index={train_index}\")\n",
    "    # print(f\"  Test:  index={test_index}\")\n",
    "\n",
    "# 查看最后一个 fold 数据帧的维度\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498e6dfd-6548-450f-9b2c-517719310b11",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#b8a1cf'>Data set tensor (math.)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "74e23923-af78-463e-a37b-ac86281ce7fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([605, 60, 4]) torch.Size([90, 60, 4]) torch.Size([605, 1]) torch.Size([90, 1])\n"
     ]
    }
   ],
   "source": [
    "# 将 NumPy数组转换为 tensor张量\n",
    "X_train_tensor = torch.from_numpy(X_train).type(torch.Tensor)\n",
    "X_test_tensor = torch.from_numpy(X_test).type(torch.Tensor)\n",
    "y_train_tensor = torch.from_numpy(y_train).type(torch.Tensor).view(-1, 1)\n",
    "y_test_tensor = torch.from_numpy(y_test).type(torch.Tensor).view(-1, 1)\n",
    "\n",
    "print(X_train_tensor.shape, X_test_tensor.shape, y_train_tensor.shape, y_test_tensor.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dffc4c5c-5845-4f34-bd6b-203269a203c8",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= '#cca3cc'>The view() function in PyTorch is used to reshape a tensor object. It’s equivalent to the reshape() function in NumPy and allows us to restructure our data to match the input shape that our BiLSTM model requires. Reshaping the data this way ensures that the BiLSTM model receives the data in the expected format. This is crucial for its ability to model the temporal dependencies in our time series data effectively.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b7436c9-8d57-4a5f-92b0-07135b6a54c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_dataset = TensorDataset(X_test_tensor, y_test_tensor)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b8a5c4-5c4e-42d3-9eb4-ec4882b6a7fe",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#b8a1cf'>Constructing a time series model</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1f412f8-3a0a-4655-a5b9-77862ecf0728",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= '#cca3cc'>双向LSTM（Bi-directional Long Short-Term Memory）网络可以通过使用nn.LSTM类并设置bidirectional=True参数来完成  \n",
    "Apply a multi-layer long short-term memory (LSTM) RNN to an input sequence. For each element in the input sequence, each layer computes the following function:\n",
    "<font face= 'Comic Sans MS' size= 3 color= '#DE3163'>$$\n",
    "\\begin{array}{ll} \\\\\n",
    "            i_t = \\sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{t-1} + b_{hi}) \\\\\n",
    "            f_t = \\sigma(W_{if} x_t + b_{if} + W_{hf} h_{t-1} + b_{hf}) \\\\\n",
    "            g_t = \\tanh(W_{ig} x_t + b_{ig} + W_{hg} h_{t-1} + b_{hg}) \\\\\n",
    "            o_t = \\sigma(W_{io} x_t + b_{io} + W_{ho} h_{t-1} + b_{ho}) \\\\\n",
    "            c_t = f_t \\odot c_{t-1} + i_t \\odot g_t \\\\\n",
    "            h_t = o_t \\odot \\tanh(c_t) \\\\\n",
    "\\end{array}\n",
    "$$\n",
    "where $h_t$ is the hidden state at time $t$, $c_t$ is the cell\n",
    "state at time $t$, $x_t$ is the input at time $t$, $h_{t-1}$\n",
    "is the hidden state of the layer at time $t-1$ or the initial hidden\n",
    "state at time $0$, and $i_t$, $f_t$, $g_t$,\n",
    "$o_t$ are the input, forget, cell, and output gates, respectively.\n",
    "$\\sigma$ is the sigmoid function, and $\\odot$ is the Hadamard product.\n",
    "\n",
    "In a multilayer LSTM, the input $x^{(l)}_t$ of the $l$ -th layer\n",
    "($l \\ge 2$) is the hidden state $h^{(l-1)}_t$ of the previous layer multiplied by\n",
    "dropout $\\delta^{(l-1)}_t$ where each $\\delta^{(l-1)}_t$ is a Bernoulli random\n",
    "variable which is $0$ with probability $dropout$.\n",
    "\n",
    "If ``proj_size > 0`` is specified, LSTM with projections will be used. This changes\n",
    "the LSTM cell in the following way. First, the dimension of $h_t$ will be changed from\n",
    "``hidden_size`` to ``proj_size`` (dimensions of $W_{hi}$ will be changed accordingly).\n",
    "Second, the output hidden state of each layer will be multiplied by a learnable projection\n",
    "matrix: $h_t = W_{hr}h_t$. Note that as a consequence of this, the output\n",
    "of LSTM network will be of different shape as well. See Inputs/Outputs sections below for exact\n",
    "dimensions of all variables. You can find more details in https://arxiv.org/abs/1402.1128."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aac70eb6-c2ab-4a6d-bb02-d4a69083bc2c",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#C2C4E2'>Model construction</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0629d7fc-2239-47e5-8adf-122ff385dbe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BiLSTM(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_layers, output_size):\n",
    "        # input_size 是输入特征的维度，hidden_size 是LSTM隐藏层神经单元维度（或称为隐藏状态的大小），\n",
    "        # num_layers 是LSTM网络层数，output_size 是输出维度\n",
    "        super(BiLSTM, self).__init__()\n",
    "        # 通过调用 super(BiLSTM, self).__init__() 初始化父类 nn.Module\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)\n",
    "        # 定义 LSTM 层，使用 batch_first=True 表示输入数据的形状是 [batch_size, seq_len(time_steps), input_size]\n",
    "        # 使用 bidirectional=True 表示的是 bidirectional LSTM，即双向 LSTM\n",
    "        self.fc = nn.Linear(hidden_size * 2, output_size)  \n",
    "        # 定义全连接层，将 LSTM 的最后一个隐藏状态映射到输出维度 output_size，双向LSTM的输出维度是隐藏层维度的两倍\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 初始化隐藏状态(hidden state)和单元状态(cell state)为全零张量\n",
    "        h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(x.device)  # 双向LSTM，所以是2倍\n",
    "        c0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(x.device)  \n",
    "\n",
    "        # LSTM输出, (output, (hn, cn))\n",
    "        out, _ = self.lstm(x, (h0, c0))  \n",
    "        # out: tensor of shape (batch_size, seq_length(time_steps), hidden_size*2)\n",
    "\n",
    "        # 取最后一个时间步的输出（或者对输出进行平均/最大池化等操作）\n",
    "        # 这里我们选择最后一个时间步的输出\n",
    "        out = self.fc(out[:, -1, :])\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f90adcb-e1c2-42c5-be47-d58ad93cf2db",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= '#DE3163'>*batch_first=True 表示输入数据的形状是 [batch_size, seq_len, input_size]，输出数据形状是 [batch_size, seq_len, num_directions * hidden_size]*  \n",
    "*batch_first=False 表示输入数据的形状是 [seq_len, batch_size, input_size]，输出数据形状是 [seq_len, batch_size, num_directions * hidden_size]*  \n",
    "*在时间序列模型中 seq_len 代表的就是时间步time_steps*</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "42b9037e-7fca-4071-a11e-42d06995c8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = BiLSTM(input_size = X_train_tensor.size(2), # 输入数据的特征数量 X_train.shape[2]\n",
    "               hidden_size = 64,\n",
    "               num_layers = 3,\n",
    "               output_size = 1)\n",
    "criterion = torch.nn.MSELoss() # 定义均方误差损失函数\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01) # 定义优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67afa51e-e46a-4e6b-ab80-2c23cf7ffc40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "BiLSTM                                   [32, 1]                   --\n",
       "├─LSTM: 1-1                              [32, 60, 128]             234,496\n",
       "├─Linear: 1-2                            [32, 1]                   129\n",
       "==========================================================================================\n",
       "Total params: 234,625\n",
       "Trainable params: 234,625\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.MEGABYTES): 450.24\n",
       "==========================================================================================\n",
       "Input size (MB): 0.03\n",
       "Forward/backward pass size (MB): 1.97\n",
       "Params size (MB): 0.94\n",
       "Estimated Total Size (MB): 2.94\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(model, (32, 60, 4)) # batch_size, seq_len(time_steps), input_size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a28d9375-f3f1-48d6-b7e4-d64fafea97b7",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#b8a1cf'>Model training</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dbef59a-4648-43b3-8314-f5db56d33a37",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#C2C4E2'>模型训练与权重更新</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "886163ec-851b-4b63-8d3e-e9b15a5451c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/20: 100%|███████████████████████████████| 19/19 [00:01<00:00, 13.24it/s]\n",
      "Epoch 2/20: 100%|███████████████████████████████| 19/19 [00:01<00:00, 14.14it/s]\n",
      "Epoch 3/20: 100%|███████████████████████████████| 19/19 [00:01<00:00, 13.10it/s]\n",
      "Epoch 4/20: 100%|███████████████████████████████| 19/19 [00:01<00:00, 14.17it/s]\n",
      "Epoch 5/20: 100%|███████████████████████████████| 19/19 [00:01<00:00, 14.00it/s]\n",
      "Epoch 6/20: 100%|███████████████████████████████| 19/19 [00:01<00:00, 14.26it/s]\n",
      "Epoch 7/20: 100%|███████████████████████████████| 19/19 [00:01<00:00, 14.48it/s]\n",
      "Epoch 8/20: 100%|███████████████████████████████| 19/19 [00:01<00:00, 13.82it/s]\n",
      "Epoch 9/20: 100%|███████████████████████████████| 19/19 [00:01<00:00, 13.80it/s]\n",
      "Epoch 10/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 14.18it/s]\n",
      "Epoch 11/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 14.16it/s]\n",
      "Epoch 12/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 14.30it/s]\n",
      "Epoch 13/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 13.36it/s]\n",
      "Epoch 14/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 12.76it/s]\n",
      "Epoch 15/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 15.39it/s]\n",
      "Epoch 16/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 14.80it/s]\n",
      "Epoch 17/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 13.82it/s]\n",
      "Epoch 18/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 12.27it/s]\n",
      "Epoch 19/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 14.48it/s]\n",
      "Epoch 20/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 13.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model saved with test loss 0.0010\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_losses = [] # 初始化列表来存储损失值\n",
    "test_losses = [] # 初始化列表来存储损失值\n",
    "best_loss = float('100') # 初始化为一个很大的值\n",
    "best_model = None # 初始化最佳模型为None\n",
    "\n",
    "# 训练循环\n",
    "num_epochs = 20  # 假设我们要训练 20个 epoch\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()  # 初始化训练进程\n",
    "    train_loss = 0.0  # 初始化每个epoch的损失总和\n",
    "\n",
    "    pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs}\",ncols=80)\n",
    "    for batch_idx, (data, target) in enumerate(pbar):\n",
    "        optimizer.zero_grad()  # 将优化器中所有参数的梯度归零\n",
    "        output = model(data)  # 每个批次的预测值\n",
    "        loss = criterion(output, target)  # 计算模型损失值(y_train_pred 与 y_train)\n",
    "        # 累加每个批次的损失\n",
    "        train_loss += loss.item() * data.size(0)\n",
    "        # 反向传播和优化\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # 更新进度条(显示当前批次的损失)\n",
    "        pbar.update()\n",
    "\n",
    "    # 计算当前epoch的平均损失\n",
    "    average_loss = train_loss / len(train_loader.dataset)\n",
    "    train_losses.append(average_loss)\n",
    "\n",
    "    model.eval()  # 将模型设置为评估模式\n",
    "    with torch.no_grad():  # 不计算梯度以节省内存和计算资源\n",
    "        test_loss = 0.0\n",
    "        for data, target in test_loader:\n",
    "            output = model(data) # 每个批次的预测值\n",
    "            loss = criterion(output, target)\n",
    "            test_loss += loss.item() * data.size(0)\n",
    "\n",
    "        # 计算测试集的平均损失\n",
    "        test_loss = test_loss / len(test_loader.dataset)\n",
    "        test_losses.append(test_loss)\n",
    "\n",
    "    # 如果测试损失比之前的最小值小，则更新最佳模型权重\n",
    "    if test_loss < best_loss:\n",
    "        best_loss = test_loss\n",
    "        best_model = model.state_dict() \n",
    "\n",
    "# 训练循环结束后保存最佳模型\n",
    "if best_model is not None:\n",
    "    torch.save(best_model, 'model_best.pth')\n",
    "    print(f'Best model saved with test loss {best_loss:.4f}')\n",
    "else:\n",
    "    print('No model saved as no improvement was made.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ee7fba3-b745-464e-b60a-496c4a56158b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best epoch: 20, with test loss: 0.0010\n"
     ]
    }
   ],
   "source": [
    "best_epoch_idx = test_losses.index(min(test_losses))\n",
    "print(f\"Best epoch: {best_epoch_idx+1}, with test loss: {min(test_losses):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "074f26b9-9cc0-4d98-b181-37dcc1b6e813",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#b8a1cf'>可视化训练过程（Loss损失）</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1f2004c9-af18-4ff5-8cc4-93432dae3c7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制损失图\n",
    "plt.figure(figsize=(18, 6))\n",
    "plt.plot(train_losses, label='Train Loss')\n",
    "plt.plot(test_losses, label='Test Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87869817-7061-4078-9fcd-3a3f313adc05",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#b89bd5'>Model evaluation</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4251582-a824-4de3-9450-1a062b189471",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#b89bd5'>Evaluation indicators (MAE、RMSE、MAPE)</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ab599c-8801-4734-abd8-996d12af2bcb",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= 'red'>These metrics each evaluate different aspects of the model’s performance:\n",
    " - <font face= 'Comic Sans MS' size= 3 color= 'orange'>RMSE: This metric calculates the square root of the average squared differences between the predicted and actual values. It gives a higher penalty for large errors.\n",
    " - <font face= 'Comic Sans MS' size= 3 color= 'gold'>MAPE: This metric computes the mean of the absolute percentage difference between the actual and predicted values. It expresses the average absolute error in terms of percentage, which can be useful when you want to understand the relative prediction error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "62cbf717-bec7-4cdc-8fb3-0b023f79f584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载最佳模型\n",
    "model.load_state_dict(torch.load('model_best.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fa0c53b2-812e-4fba-8d5e-38162ae18c9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating: 100%|███████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 47.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.0241\n",
      "RMSE: 0.0321\n",
      "MAPE: 3.2193%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def mean_absolute_percentage_error(y_true, y_pred):\n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    mask = y_true != 0\n",
    "    return np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100 if mask.any() else 0\n",
    "\n",
    "# 将模型设置为评估模式\n",
    "model.eval()\n",
    "y_pred_all = []\n",
    "y_true_all = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    pbar = tqdm(test_loader, desc='Evaluating')\n",
    "    for data, target in pbar:\n",
    "        y_pred = model(data).detach().cpu().numpy()  # 确保张量在CPU上\n",
    "        y_true = target.detach().cpu().numpy()  # 确保张量在CPU上\n",
    "        y_pred_all.append(y_pred)\n",
    "        y_true_all.append(y_true)\n",
    "        pbar.update()\n",
    "\n",
    "# 合并所有批次的预测和真实值\n",
    "y_pred_all = np.concatenate(y_pred_all)\n",
    "y_true_all = np.concatenate(y_true_all)\n",
    "\n",
    "mae = np.mean(np.abs(y_pred_all - y_true_all))\n",
    "print(f\"MAE: {mae:.4f}\")\n",
    "\n",
    "rmse = np.sqrt(np.mean((y_pred_all - y_true_all) ** 2))\n",
    "print(f\"RMSE: {rmse:.4f}\")\n",
    "\n",
    "mape = mean_absolute_percentage_error(y_true_all, y_pred_all)\n",
    "print(f\"MAPE: {mape:.4f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9ee265f-3a4a-4ea9-8b85-7178f528224b",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#b89bd5'>Inverse normalisation (.math)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d7f954ff-4747-46dc-adeb-713feb92adbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取Close价格的反归一化参数\n",
    "close_denorm_param = scale_params[3]  # 假设Close是第四个特征\n",
    "\n",
    "# 如果反归一化包括一个偏移量shift，需要再加上 shift_param\n",
    "# denormalized_predictions = (predictions[:, 0] * scale_param) + shift_param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d59571f7-1ba2-44fb-b591-4169395e5669",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将训练预测 Close价格的反归一化\n",
    "y_train_pred = model(X_train_tensor).detach().numpy()\n",
    "y_train_denormalized_predictions = (y_train_pred * scale_params[3]) + original_min[3]\n",
    "\n",
    "# 将测试预测 Close价格的反归一化\n",
    "y_test_pred = model(X_test_tensor).detach().numpy()\n",
    "y_test_denormalized_predictions = (y_test_pred * scale_params[3]) + original_min[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c997ff6-c08a-40ff-9402-d26b186d0137",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#b89bd5'>Visualisation of results</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9686ab7-6a7b-4854-9e61-a6dda51b8c7f",
   "metadata": {},
   "source": [
    "计算训练预测与测试预测的绘图数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fc6149eb-ae05-4da8-a9be-28e4f2e24a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredict = AAPL[window_size:X_train.shape[0]+X_train.shape[1]]\n",
    "trainPredictPlot = trainPredict.assign(TrainPrediction= y_train_denormalized_predictions)\n",
    "\n",
    "testPredict = AAPL[X_train.shape[0]+X_train.shape[1]:]\n",
    "testPredictPlot = testPredict.assign(TestPrediction= y_test_denormalized_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2718274d-e874-4f53-a1c5-cd22502be9ba",
   "metadata": {},
   "source": [
    "绘制模型收盘价格的原始数据与预测数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4fa553f0-d4aa-4cc3-ad3b-f8fd61d5a09b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data\n",
    "plt.figure(figsize=(18,6))\n",
    "plt.title('BiLSTM Close Price Validation')\n",
    "plt.plot(AAPL['Close'], color='blue', label='original')\n",
    "plt.plot(trainPredictPlot['TrainPrediction'], color='orange',label='Train Prediction')\n",
    "plt.plot(testPredictPlot['TestPrediction'], color='red', label='Test Prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec6ae4df-d92a-4dca-a43f-228f06de57be",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#C2C4E2'>Model prediction</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "82a44bfb-23ac-47ca-912f-10ee0d0373d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 60, 4])\n"
     ]
    }
   ],
   "source": [
    "# 假设latest_closes是一个包含最新window_size个收盘价的列表或数组\n",
    "latest_closes = AAPL[['Open', 'High', 'Low', 'Close']][-window_size:].values\n",
    "scaled_latest_closes = scaler.transform(latest_closes)\n",
    "tensor_latest_closes = torch.from_numpy(scaled_latest_closes).type(torch.Tensor).view(1, window_size, 4) \n",
    "print(tensor_latest_closes.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "95a9e3bd-fa5a-401f-b58a-b6f110db4502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[165.9167]], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用模型预测下一个时间点的收盘价\n",
    "next_close_pred = model(tensor_latest_closes).detach().numpy()\n",
    "next_close_denormalized_pred = (next_close_pred * scale_params[3]) + original_min[3]\n",
    "next_close_denormalized_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d7d6f7-57ad-4ff3-be25-ea16c8d69de6",
   "metadata": {},
   "source": [
    "scaler.fit_transform(X):\n",
    "这个方法首先会计算数据集 X 的统计信息（例如，对于 MinMaxScaler 是最小值和最大值；对于 StandardScaler 是均值和标准差），然后用这些统计信息来转换 X 中的数据。这通常用于训练集，因为您既需要拟合（计算统计信息）也需要转换（应用这些统计信息）。\n",
    "\n",
    "scaler.fit(X_train):\n",
    "这个方法只计算数据集 X_train 的统计信息，但不转换数据。您通常会在训练集上调用 fit 方法来准备缩放器。\n",
    "\n",
    "scaler.transform(X_test):\n",
    "在已经使用 fit 方法拟合了缩放器之后，您可以使用 transform 方法来转换新的数据（例如测试集或预测时的数据）。此时，缩放器会使用在 fit 方法期间计算的统计信息来转换 X_test 中的数据。\n",
    "\n",
    "如果您已经在一个数据集（例如训练集）上拟合了缩放器 scaler，并且想要对 latest_closes（可能是测试集的一部分或实时数据）进行缩放，您应该使用 scaler.transform(latest_closes) 而不是 scaler.fit_transform(latest_closes)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfa4677f-8898-4740-a16b-d7c50b7435eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
